Digital image correlation based crack monitoring on masonry arch bridges
Year: 2024
Type: article
Abstract: Masonry arch bridges remain a vital part of the UK’s transportation network. The combination of increased traffic loads and more frequent climatic and anthropogenic disasters has heightened the risk of structural failures in these aging infrastructures, making effective monitoring essential for maintaining their safety and operational performance. In this study, a large-scale masonry arch bridge was constructed and tested under controlled laboratory conditions. Static patch loads were applied both centrically and eccentrically to the top of the backfill to induce damage accumulation. A Digital image correlation (DIC) set up was utilized to monitor the initiation and propagation of full-field strain/cracking in the spandrel wall of the bridge during different magnitudes of loading and unloading states. Comparing DIC data with readings from displacement gauges, the results confirmed that DIC could measure the deformations of the untreated surface with satisfied accuracy, using the inherent brick bond patterns and natural characteristics of the masonry surface as tracking points. DIC results provided valuable insights into the cracking behaviour of the masonry arch bridge throughout the entire loading–unloading states, offering important information on the load levels that induced the onset and the activation of hinges. Additionally, the interaction between the arch barrel and spandrel wall, as well as the crack mechanism in the spandrel wall, were analysed. The findings reported in the study demonstrate the significant potential of DIC for real-time non-contact monitoring of real masonry arch bridges, offering a better understanding of their structural behaviour under various loading scenarios.
Source: Engineering Failure Analysis
Authors: Bowen Liu, Joshua Collier, Vasilis Sarhosis
Institution: University of Leeds
Language: English
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,710
OpenAlex ID: W4405097714